\begin{table}[h!]
\centering
\caption{Average Treatment Effect: Gender Outreach}
\centering
\begin{tabular}[t]{lcc}
\toprule
& \multicolumn{2}{c}{\textit{Dependent Variable}}\\
  & Interview Scheduled & Email Response\\
\midrule
Jake Miller & \num{-0.419}* & \num{-0.299}\\
 & (\num{0.254}) & (\num{0.199})\\
\midrule
Num.Obs. & \num{173} & \num{173}\\
\bottomrule
\multicolumn{3}{l}{\rule{0pt}{1em}* p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01}\\
\end{tabular}
\begin{quote}
    \footnotesize \textit{Note:} This table shows the pre-registered probit regression results to show robustness. The independent variable is the outreach gender -- 1 if ``Jake Miller'' and 0 if ``Mary Williams.'' The first model uses the dependent variable of whether the elite scheduled and attended an interview, taking the value of 1 if yes, 0 if not. The second model uses the dependent variable of whether the elite responded to the email, taking the value of 1 if yes and 0 if not.
\end{quote}
\end{table}
